(1) Field of the Invention
The present invention relates generally to the field of sonar signal processing and, more particularly, to a method for processing very small data distribution sets, e.g., data sets with less than ten to fifteen measurements.
(2) Description of the Prior Art
In some cases it may be very important and/or critical to know whether data received by a sonar system is simply random noise, which may be a false alarm, or is more likely due to the detection of a submarine or other vessel of interest. Naval sonar systems require that signals be classified according to structure; i.e., periodic, transient, random or chaotic.
Research has revealed a critical need for highly sparse data set time distribution analysis methods and apparatus separate and apart from those adapted for treating large sample distributions. It is well known that large sample methods often fail when applied to small sample distributions, but that the same is not necessarily true for small sample methods applied to large data sets. Very small data set distributions may be defined as those with less than about ten (10 measurement (data) points.
One reference is entitled “Enhanced System And Method For Processing Signals To Determine Their Stochastic Properties”, U.S. Pat. No. 5,966,414, issued 12 Oct. 1999, to the present inventor, which is discussed below briefly. More generally, theoretical and practical statistical considerations are contained in standard reference works such as P. J. Hoel et al., Introduction to the Theory of Probability, and Introduction to Statistical Theory/Boston, Houghton-Mifflin, 1971. The theoretical framework of elementary stochastic (Poisson) processes is known, although the application of the theory in practice is often cumbersome. A variety of methods such as spectral analysis are available to the undersea warfare analyst for processing signals.
The term “randomness” encompasses different meanings in science and engineering. In engineering applications, Cartesian coordinate distributions may be thought of as “purely random” if they conform to a “white noise” structure, regardless of the underlying generating mechanism that produced the “noise.” Our use of the term “random” (or “randomness”) is defined in terms of a “random process.” as measured by a probability distribution model, namely a nearest-neighbor stochastic (Poisson) process. One may think of pure randomness in a pragmatic manner as a distribution for which no function, mapping, or relation can be constructed that provides meaningful insight into the underlying structure; for example, no prediction model can be generated. Also, we must distinguish the terms “stochastic randomness” from “deterministic randomness (chaos)” as generally described in U.S. Pat. No. 5,781,460, F. J. O'Brien, Jr., et al. and/or other of the related applications cited above or patents cited hereinafter. For instance, the present invention is utilized for classifying the statistical characteristics of a signal as “randomness” while other patents, such as U.S. Pat. No. 5,781,460, F. J. O'Brien, Jr., et al., may be utilized for identifying “chaos.”
Examples of exemplary patents related to the general field of the endeavor of analysis of sonar signals include:
U.S. Pat. No. 5,675,553, issued Oct. 7, 1997, to O'Brien, Jr. et al., discloses a method for filling in missing data intelligence in a quantified time-dependent data signal that is generated by, e.g., an underwater acoustic sensing device. In accordance with one embodiment of the invention, this quantified time-dependent data signal is analyzed to determine the number and location of any intervals of missing data, i.e., gaps in the time series data signal caused by noise in the sensing equipment or the local environment. The quantified time-dependent data signal is also modified by a low pass filter to remove any undesirable high frequency noise components within the signal. A plurality of mathematical models are then individually tested to derive an optimum regression curve for that model, relative to a selected portion of the signal data immediately preceding each previously identified data gap. The aforesaid selected portion is empirically determined on the basis of a data base of signal values compiled from actual undersea propagated signals received in cases of known target motion scenarios. An optimum regression curve is that regression curve, linear or nonlinear, for which a mathematical convergence of the model is achieved. Convergence of the model is determined by application of a smallest root-mean-square analysis to each of the plurality of models tested. Once a model possessing the smallest root-mean-square value is derived from among the plurality of models tested, that optimum model is then selected, recorded, and stored for use in filling the data gap. This process is then repeated for each subsequent data gap until all of the identified data gaps are filled.
U.S. Pat. No. 5,703,906, issued Dec. 30, 1997, to O'Brien, Jr. et al., discloses a signal processing system which processes a digital signal, generally in response to an analog signal which includes a noise component and possibly also an information component representing three mutually orthogonal items of measurement information represented as a sample point in a symbolic Cartesian three-dimensional spatial reference system. A noise likelihood determination sub-system receives the digital signal and generates a random noise assessment of whether or not the digital signal comprises solely random noise, and if not, generates an assessment of degree-of-randomness. The noise likelihood determination system controls the operation of an information processing sub-system for extracting the information component in response to the random noise assessment or a combination of the random noise assessment and the degree-of-randomness assessment. The information processing system is illustrated as combat control equipment for submarine warfare, which utilizes a sonar signal produced by a towed linear transducer array, and whose mode operation employs three orthogonally related dimensions of data, namely: (i) clock time associated with the interval of time over which the sample point measurements are taken, (ii) conical angle representing bearing of a passive sonar contact derived from the signal produced by the towed array, and (iii) a frequency characteristic of the sonar signal.
U.S. Pat. No. 5,966,414, issued Oct. 12, 1999, to Francis J. O'Brien, Jr., discloses a signal processing system which processes a digital signal generated in response to an analog signal which includes a noise component and possibly also an information component. An information processing sub-system receives said digital signal and processes it to extract the information component. A noise likelihood determination sub-system receives the digital signal and generates a random noise assessment that the digital signal comprises solely random noise, and controls the operation of the information processing sub-system in response to the random noise assessment.
U.S. Pat. No. 5,781,460, issued Jul. 14, 1998, to Nguyen et al., discloses a chaotic signal processing system which receives an input signal from a sensor in a chaotic environment and performs a processing operation in connection therewith to provide an output useful in identifying one of a plurality of chaotic processes in the chaotic environment. The chaotic signal processing system comprises an input section, a processing section and a control section. The input section is responsive to input data selection information for providing a digital data stream selectively representative of the input signal provided by the sensor or a synthetic input representative of a selected chaotic process. The processing section includes a plurality of processing modules each for receiving the digital data stream from the input means and for generating therefrom an output useful in identifying one of a plurality of chaotic processes. The processing section is responsive to processing selection information to select one of the plurality of processing modules to provide the output. The control module generates the input data selection information and the processing selection information in response to inputs provided by an operator.
U.S. Pat. No. 5,963,591, issued Oct. 5, 1999, to O'Brien, Jr. et al., discloses a signal processing system which processes a digital signal generally in response to an analog signal which includes a noise component and possibly also an information component representing four mutually orthogonal items of measurement information representable as a sample point in a symbolic four-dimensional hyperspatial reference system. An information processing and decision sub-system receives said digital signal and processes it to extract the information component. A noise likelihood determination sub-system receives the digital signal and generates a random noise assessment of whether or not the digital signal comprises solely random noise, and if not, generates an assessment of degree-of-randomness. The noise likelihood determination system controls whether or not the information processing and decision sub-system is used, in response to one or both of these generated outputs. One prospective practical application of the invention is the performance of a triage function upon signals from sonar receivers aboard naval submarines, to determine suitability of the signal for feeding to a subsequent contact localization and motion analysis (CLMA) stage.
U.S. Pat. No. 6,397,234, issued May 28, 2002, to O'Brien, Jr. et al., discloses a method and apparatus are provided for automatically characterizing the spatial arrangement among the data points of a time series distribution in a data processing system wherein the classification of said time series distribution is required. The method and apparatus utilize a grid in Cartesian coordinates to determine (1) the number of cells in the grid containing at least-one input data point of the time series distribution; (2) the expected number of cells which would contain at least one data point in a random distribution in said grid; and (3) an upper and lower probability of false alarm above and below said expected value utilizing a discrete binomial probability relationship in order to analyze the randomness characteristic of the input time series distribution. A labeling device also is provided to label the time series distribution as either random or nonrandom.
U.S. Pat. No. 5,144,595, issued Sep. 1, 1992, to Graham et al., discloses an adaptive statistical filter providing improved performance target motion analysis noise discrimination includes a bank of parallel Kalman filters. Each filter estimates a statistic vector of specific order, which in the exemplary third order bank of filters of the preferred embodiment, respectively constitute coefficients of a constant, linear and quadratic fit. In addition, each filter provides a sum-of-squares residuals performance index. A sequential comparator is disclosed that performs a likelihood ratio test performed pairwise for a given model order and the next lowest, which indicates whether the tested model orders provide significant information above the next model order. The optimum model order is selected based on testing the highest model orders. A robust, unbiased estimate of minimal rank for information retention providing computational efficiency and improved performance noise discrimination is therewith accomplished.
U.S. Pat. No. 5,757,675, issued May 26, 1998, to O'Brien, Jr., discloses an improved method for laying out a workspace using the prior art crowding index, PDI, where the average interpoint distance between the personnel and/or equipment to be laid out can be determined. The improvement lies in using the convex hull area of the distribution of points being laid out within the workplace space to calculate the actual crowding index for the workspace. The convex hull area is that area having a boundary line connecting pairs of points being laid out such that no line connecting any pair of points crosses the boundary line. The calculation of the convex hull area is illustrated using Pick's theorem with additional methods using the Surveyor's Area formula and Hero's formula.
U.S. Pat. No. 6,466,516, issued Oct. 5, 1999, to O'Brien, Jr. et al., discloses a method and apparatus for automatically characterizing the spatial arrangement among the data points of a three-dimensional time series distribution in a data processing system wherein the classification of the time series distribution is required. The method and apparatus utilize grids in Cartesian coordinates to determine (1) the number of cubes in the grids containing at least one input data point of the time series distribution; (2) the expected number of cubes which would contain at least one data point in a random distribution in said grids; and (3) an upper and lower probability of false alarm above and below said expected value utilizing a discrete binomial probability relationship in order to analyze the randomness characteristic of the input time series distribution. A labeling device also is provided to label the time series distribution as either random or nonrandom, and/or random or nonrandom within what probability, prior to its output from the invention to the remainder of the data processing system for further analysis.
The above cited art, while extremely useful under certain circumstances, does not provide sufficient flexibility to measure the spatial arrangement among a very small to large number of points, objects, measurements, and the like, whereby an ascertainment of the mathematical property of randomness (or noise-degree) may be made. This improvement over the standard prior art provides the capability to use mathematical statistical methods for highly sparse data sets (e.g., <10-15 sample points). The existence of such sparse data sets requires methods appropriate for processing them reliably and validly. Moreover, the method is also applicable for larger data sets and may provide a higher degree of precision in the “random-not random” decision. Consequently, those of skill in the art will appreciate the present invention which addresses these and other problems.